Adversarial Images for Variational Autoencoders

TL;DR

This paper explores adversarial attacks on autoencoders, demonstrating their robustness compared to classifiers and analyzing the trade-offs between input distortion and output misdirection.

Contribution

It introduces a novel adversarial attack method targeting internal latent representations of autoencoders and evaluates their robustness across datasets.

Findings

Autoencoders are more robust to adversarial attacks than classifiers.

A trade-off exists between input distortion and similarity to target images.

Regular autoencoders show consistent robustness across datasets.

Abstract

We investigate adversarial attacks for autoencoders. We propose a procedure that distorts the input image to mislead the autoencoder in reconstructing a completely different target image. We attack the internal latent representations, attempting to make the adversarial input produce an internal representation as similar as possible as the target's. We find that autoencoders are much more robust to the attack than classifiers: while some examples have tolerably small input distortion, and reasonable similarity to the target image, there is a quasi-linear trade-off between those aims. We report results on MNIST and SVHN datasets, and also test regular deterministic autoencoders, reaching similar conclusions in all cases. Finally, we show that the usual adversarial attack for classifiers, while being much easier, also presents a direct proportion between distortion on the input, and misdirection on the output. That proportionality however is hidden by the normalization of the output, which maps a linear layer into non-linear probabilities.